{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T13:56:54Z","timestamp":1764251814821,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province","doi-asserted-by":"publisher","award":["YQ2021F005","6142A0103011"],"award-info":[{"award-number":["YQ2021F005","6142A0103011"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Laboratory of Science and Technology on Remote Sensing Information and Image Analysis Foundation Project","award":["YQ2021F005","6142A0103011"],"award-info":[{"award-number":["YQ2021F005","6142A0103011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Generalized stereo matching faces the radiation difference and small ground feature difference brought by different satellites and different time phases, while the texture-less and disparity discontinuity phenomenon seriously affects the correspondence between matching points. To address the above problems, a novel generalized stereo matching method based on the iterative optimization of hierarchical graph structure consistency cost is proposed for urban 3D scene reconstruction. First, the self-similarity of images is used to construct k-nearest neighbor graphs. The left-view and right-view graph structures are mapped to the same neighborhood, and the graph structure consistency (GSC) cost is proposed to evaluate the similarity of the graph structures. Then, cross-scale cost aggregation is used to adaptively weight and combine multi-scale GSC costs. Next, object-based iterative optimization is proposed to optimize outliers in pixel-wise matching and mismatches in disparity discontinuity regions. The visibility term and the disparity discontinuity term are iterated to continuously detect occlusions and optimize the boundary disparity. Finally, fractal net evolution is used to optimize the disparity map. This paper verifies the effectiveness of the proposed method on a public US3D dataset and a self-made dataset, and compares it with state-of-the-art stereo matching methods.<\/jats:p>","DOI":"10.3390\/rs15092369","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T12:10:03Z","timestamp":1682943003000},"page":"2369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Generalized Stereo Matching Method Based on Iterative Optimization of Hierarchical Graph Structure Consistency Cost for Urban 3D Reconstruction"],"prefix":"10.3390","volume":"15","author":[{"given":"Shuting","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, China"}]},{"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, China"}]},{"given":"Wen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150006, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, Y., and Wu, B. (2021). Relation-constrained 3D reconstruction of buildings in metropolitan areas from photogrammetric point clouds. Remote Sens., 13.","DOI":"10.3390\/rs13010129"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Qi, Z., Zou, Z., and Chen, H. (2022). 3D Reconstruction of Remote Sensing Mountain Areas with TSDF-Based Neural Networks. Remote Sens., 14.","DOI":"10.3390\/rs14174333"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"331","DOI":"10.5194\/isprs-archives-XLII-2-W17-331-2019","article-title":"Open-source image-based 3D reconstruction pipelines: Review, comparison and evaluation","volume":"42","author":"Stathopoulou","year":"2019","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xiao, X., Guo, B., Li, D., Li, L., Yang, N., Liu, J., Zhang, P., and Peng, Z. (2016). Multi-view stereo matching based on self-adaptive patch and image grouping for multiple unmanned aerial vehicle imagery. Remote Sens., 8.","DOI":"10.3390\/rs8020089"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nguatem, W., and Mayer, H. (2017, January 22\u201329). Modeling urban scenes from Pointclouds. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.414"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"75","DOI":"10.5194\/isprsarchives-XXXIX-B3-75-2012","article-title":"Fully automated generation of accurate digital surface models with sub-meter resolution from satellite imagery","volume":"XXXIX-B1","author":"Wohlfeil","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29699","DOI":"10.1109\/ACCESS.2019.2902249","article-title":"Novel belief propagation algorithm for stereo matching with a robust cost computation","volume":"7","author":"Pan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TIP.2015.2395820","article-title":"Accurate stereo matching by twostep energy minimization","volume":"24","author":"Mozerov","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, H., Wang, R., Xia, Y., and Zhang, X. (2020). Improved cost computation and adaptive shape guided filter for local stereo matching of low texture stereo images. Appl. Sci., 10.","DOI":"10.3390\/app10051869"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2154010","DOI":"10.1142\/S0218001421540100","article-title":"Local stereo matching: An adaptive weighted guided image filtering-based approach","volume":"35","author":"Zhang","year":"2021","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1109\/TPAMI.2012.156","article-title":"Fast cost-volume filtering for visual correspondence and beyond","volume":"35","author":"Hosni","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, C., Li, Z., Cheng, Y., Cai, R., Chao, H., and Rui, Y. (2015, January 13\u201316). Meshstereo: A global stereo model with mesh alignment regularization for view interpolation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.238"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s11554-021-01180-1","article-title":"Dynamic programming with adaptive and self-adjusting penalty for real-time accurate stereo matching","volume":"19","author":"Hallek","year":"2022","journal-title":"J. Real-Time Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"570","DOI":"10.3390\/sym11040570","article-title":"Stereo Matching Methods for Imperfectly Rectified Stereo Images","volume":"11","author":"Nguyen","year":"2019","journal-title":"Symmetry"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1049\/el.2016.1731","article-title":"PatchMatch belief propagation meets depth upsampling for high-resolution depth maps","volume":"52","author":"Shin","year":"2016","journal-title":"Electron. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.patrec.2018.07.020","article-title":"A hierarchical stereo matching algorithm based on adaptive support region aggregation method","volume":"112","author":"Zeglazi","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1953","DOI":"10.1007\/s12652-021-02958-8","article-title":"An edge-aware based adaptive multi-feature set extraction for stereo matching of binocular images","volume":"13","author":"Haq","year":"2022","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","article-title":"Stereo processing by semiglobal matching and mutual information","volume":"30","author":"Hirschmuller","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, W., Li, X., Yang, B., and Fu, Y. (2020). A Novel Stereo Matching Algorithm for Digital Surface Model (DSM) Generation in Water Areas. Remote Sens., 12.","DOI":"10.3390\/rs12050870"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khamis, S., Fanello, S., Rhemann, C., Kowdle, A., Valentin, J., and Izadi, S. (2018, January 8\u201314). StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01267-0_35"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Xu, B., Xu, Y., Yang, X., Jia, W., and Guo, Y. (2021). Bilateral grid learning for stereo matching network. arXiv.","DOI":"10.1109\/CVPR46437.2021.01231"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3042202","article-title":"Double Propagation Stereo Matching for Urban 3-D Reconstruction from Satellite Imagery","volume":"60","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1841","DOI":"10.1109\/LGRS.2020.3008268","article-title":"High-Resolution Satellite Stereo Matching by Object-Based Semiglobal Matching and Iterative Guided Edge-Preserving Filter","volume":"18","author":"Tatar","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.isprsjprs.2022.04.020","article-title":"HMSM-Net: Hierarchical multi-scale matching network for disparity estimation of high-resolution satellite stereo images","volume":"188","author":"He","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, S., Zhou, R., Li, S., Jiang, S., and Jiang, W. (2021). Disparity Estimation of High-Resolution Remote Sensing Images with Dual-Scale Matching Network. Remote Sens., 13.","DOI":"10.3390\/rs13245050"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, W., Chen, H., and Yang, S. (2022). Self-Supervised Stereo Matching Method Based on SRWP and PCAM for Urban Satellite Images. Remote Sens., 14.","DOI":"10.3390\/rs14071636"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhang, C., Cui, Y., Zhu, Z., Jiang, S., and Jiang, W. (2022). Building Height Extraction from GF-7 Satellite Images Based on Roof Contour Constrained Stereo Matching. Remote Sens., 14.","DOI":"10.3390\/rs14071566"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104903","DOI":"10.1016\/j.compag.2019.104903","article-title":"DSM and DTM generation from VHR satellite stereo imagery over plastic covered greenhouse areas","volume":"164","author":"Nemmaoui","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","first-page":"31","article-title":"Precision analysis of 3D reconstruction model of generalized stereo image pair","volume":"35","author":"Wang","year":"2010","journal-title":"Sci. Surv. Mapp."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yan, Y., Su, N., Zhao, C., and Wang, L. (2017). A Dynamic Multi-Projection-Contour Approximating Framework for the 3D Reconstruction of Buildings by Super-Generalized Optical Stereo-Pairs. Sensors, 17.","DOI":"10.3390\/s17092153"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1109\/TGRS.2013.2249521","article-title":"Generation and Quality Assessment of Stereo-Extracted DSM From GeoEye-1 and WorldView-2 Imagery","volume":"52","author":"Aguilar","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, B., Yu, H., and Qi, G. (2022, January 19\u201324). GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01267"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, X., Bai, X., Wang, C., Huang, L., Chen, Y., and Hancock, E.R. (2022, January 19\u201324). Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01266"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.imavis.2015.01.003","article-title":"Robust stereo matching using adaptive random walk with restart algorithm","volume":"37","author":"Lee","year":"2015","journal-title":"Image Vis. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1109\/TPAMI.2003.1206509","article-title":"Stereo matching using belief propagation","volume":"25","author":"Sun","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6937","DOI":"10.1109\/TGRS.2017.2737033","article-title":"Ossim: An object-based multiview stereo algorithm using ssim index matching cost","volume":"99","author":"Fei","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","unstructured":"Liang, Z., Feng, Y., Guo, Y., Liu, H., Qiao, L., Chen, W., and Zhang, J. (2017). Learning deep correspondence through prior and posterior feature constancy. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3331","DOI":"10.1109\/TIP.2017.2687101","article-title":"Robust efficient depth reconstruction with hierarchical confidence-based matching","volume":"26","author":"Li","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bosch, M., Foster, K., Christie, G., Wang, S., Hager, G.D., and Brown, M. (2019, January 7\u201311). Semantic stereo for incidental satellite images. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA.","DOI":"10.1109\/WACV.2019.00167"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhang, D., Xie, F., and Zhang, L. (2018, January 6\u20138). Preprocessing and fusion analysis of GF-2 satellite Remote-sensed spatial data. Proceedings of the 2018 International Conference on Information Systems and Computer Aided Education (ICISCAE), Changchun, China.","DOI":"10.1109\/ICISCAE.2018.8666873"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Huang, B., Zheng, J., Giannarou, S., and Elson, D.S. (2022, January 19\u201324). H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00492"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, Y., Lu, Y., and Lu, G. (2021, January 6\u201311). Stereo Rectification Based on Epipolar Constrained Neural Network. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9413735"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"162884","DOI":"10.1109\/ACCESS.2021.3133664","article-title":"An Epipolar Resampling Method for Multi-View High Resolution Satellite Images Based on Block","volume":"9","author":"Yi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1007\/s11075-011-9498-x","article-title":"A new Tikhonov regularization method","volume":"59","author":"Fuhry","year":"2012","journal-title":"Numer. Algorithms"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2020.05.011","article-title":"An automated PCA-based approach towards optization of the rational function model","volume":"165","author":"Gholinejad","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/TCSVT.2015.2513663","article-title":"Cross-Scale Cost Aggregation for Stereo Matching","volume":"27","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1109\/LGRS.2012.2194693","article-title":"A Spatially-Constrained Color-Texture Model for Hierarchical VHR Image Segmentation","volume":"10","author":"Hu","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5109","DOI":"10.1109\/TGRS.2020.2972312","article-title":"Dense Stereo Matching Strategy for Oblique Images That Considers the Plane Directions in Urban Areas","volume":"58","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1016\/j.isprsjprs.2009.02.003","article-title":"Accuracy assessment of digital elevation models by means of robust statistical methods","volume":"64","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2369\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:27:14Z","timestamp":1760124434000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2369"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":50,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092369"],"URL":"https:\/\/doi.org\/10.3390\/rs15092369","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,4,30]]}}}